1. Field of the Invention
This invention relates generally to semiconductor manufacturing, and, more particularly, to a method, system, and apparatus for performing a process to improve fault detection reliability through feedback.
2. Description of the Related Art
The technology explosion in the manufacturing industry has resulted in many new and innovative manufacturing processes. Today's manufacturing processes, particularly semiconductor manufacturing processes, call for a large number of important steps. These process steps are usually vital, and therefore, require a number of inputs that are generally fine-tuned to maintain proper manufacturing control.
The manufacture of semiconductor devices requires a number of discrete process steps to create a packaged semiconductor device from raw semiconductor material. The various processes, from the initial growth of the semiconductor material, the slicing of the semiconductor crystal into individual wafers, the fabrication stages (etching, doping, ion implanting, or the like), to the packaging and final testing of the completed device, are so different from one another and specialized that the processes may be performed in different manufacturing locations that contain different control schemes.
Generally, a set of processing steps is performed across a group of semiconductor wafers, sometimes referred to as a lot. For example, a process layer that may be composed of a variety of different materials may be formed across a semiconductor wafer. Thereafter, a patterned layer of photoresist may be formed across the process layer using known photolithography techniques. Typically, an etch process is then performed across the process layer using the patterned layer of photoresist as a mask. This etching process results in the formation of various features or objects in the process layer. Such features may be used as, for example, a gate electrode structure for transistors. Many times, trench isolation structures are also formed in various regions of the semiconductor wafer to create electrically isolated areas across a semiconductor wafer. One example of an isolation structure that can be used is a shallow trench isolation (STI) structure.
The manufacturing tools within a semiconductor manufacturing facility typically communicate with a manufacturing framework or a network of processing modules. Each manufacturing tool is generally connected to an equipment interface. The equipment interface is connected to a machine interface to which a manufacturing network is connected, thereby facilitating communications between the manufacturing tool and the manufacturing framework. The machine interface can generally be part of an advanced process control (APC) system. The APC system initiates a control script, which can be a software program that automatically retrieves the data needed to execute a manufacturing process.
FIG. 1 illustrates a typical semiconductor wafer 105. The semiconductor wafer 105 typically includes a plurality of individual semiconductor die 103 arranged in a grid 150. Using known photolithography processes and equipment, a patterned layer of photoresist may be formed across one or more process layers that are to be patterned. As part of the photolithography process, an exposure process is typically performed by a stepper on approximately one to four die 103 locations at a time, depending on the specific photomask employed. The patterned photoresist layer can be used as a mask during etching processes, wet or dry, performed on the underlying layer or layers of material, e.g., a layer of polysilicon, metal, or insulating material, to transfer the desired pattern to the underlying layer. The patterned layer of photoresist is comprised of a plurality of features, e.g., line-type features or opening-type features that are to be replicated in an underlying process layer.
When processing semiconductor wafers, various measurements relating to the process results on the semiconductor wafers, as well as conditions of the processing tool in which the wafers are processed, are acquired and analyzed. The analysis is then used to modify subsequent processes. Turning now to FIG. 2, a flow chart depiction of a state-of-the-art process flow is illustrated. A processing system may process various semiconductor wafers 105 in a lot of wafers (block 210). Upon processing of the semiconductor wafers 105, the processing system may acquire metrology data relating to the processing of the semiconductor wafers 105 from selected wafers in the lot (block 220). Additionally, the processing system may acquire tool state sensor data from the processing tool used to process the wafers (block 230). Tool state sensor data may include various tool state parameters such as pressure data, humidity data, temperature data, and the like.
Based upon the metrology data and the tool state data, the processing system may perform fault detection to acquire data relating to faults associated with the processing of the semiconductor wafers 105 (block 240). Upon detecting various faults associated with processing of the semiconductor wafers 105, the processing system may perform a principal component analysis (“PCA”) relating to the faults (block 250). Principal component analysis (PCA) is a multivariate technique that models the correlation structure in the data by reducing the dimensionality of the data. The correlation may take on various forms, such as correlation of problems with the processed wafers with problems in the processing tool. The PCA may provide an indication of the types of corrections that may be useful in processing subsequent semiconductor wafers 105. Upon performing the PCA, the processing system may perform subsequent processes upon the semiconductor wafers 105 with various adjustments being based upon the PCA (block 260). The PCA performs an analysis to determine whether there are abnormal conditions that may exist with respect to a tool. Upon detecting any abnormal conditions, various signals may be issued, indicating to the operators that various faults have been detected.
One issue associated with state-of-the-art methods includes the fact that a determination of what constitutes an abnormal correlation may be based upon data used to build a fault detection model or a PCA model used to perform the fault detection analysis and the PCA. Generally, the abnormal conditions detected by performing the PCA may be statistically different from the data that may have been used to build the fault detection or the PCA model. The term “statistically different” may mean a variety of statistical differences, such as differences based upon population mean, variance, etc. These abnormal conditions may not be an accurate reflection of the true manner of operation in which the tool is performing. For example, if during the development of the fault detection model or the PCA model, the values for a pressure sensor were held within small constraints, larger variations in the pressure during the actual processing would generally be identified as a significant fault. The problem with this methodology is that if the larger variation of the pressure did not result in any negative impact to the material being processed, then the fault indication may be false. In other words, if the larger variation was still small enough that no significant impact to the process was present, a false-positive fault indication occurs. This false-positive introduces inefficiencies and idle times in a manufacturing setting.
More recently, various efforts have been made to incorporate weighting schemes into PCA. The weighting schemes may provide a significant difference in weight attached to various parameters, such as the pressure. However, the problems associated with the state-of-the-art weighting schemes include the fact that prior knowledge is required to assign a predetermined weight to a particular parameter. For example, prior knowledge may indicate that a smaller amount of weight should be assigned to the pressure parameter during the PCA analysis relating to a particular process. This would reduce false indications due to variations in pressure that may have been harmless. However, this methodology can be an inefficient, cumbersome task and, at best, may involve guess work. Furthermore, it may not be readily clear if adjusting the weight to particular parameters would result in improved or worsened PCA relating to a particular process.
The present invention is directed to overcoming, or at least reducing, the effects of, one or more of the problems set forth above.